# 187th Place Approach + Some Insights

Nothing to fancy for us, but I thought I'd share our approach.
For our phase 2 kernels, we selected 2 different ensembles of 2 Effiecientnets: B5 (Image size 456) + B4 (Image size 328) with pretty standard augmentations and some TTA.
We also experimented with a few values for the GaussianBlur and ended up going with a value of 20 (empirically got slightly better results).
We actually decided to only use the current competition data (no 2015 data).
I wonder if there are any top solutions who did the same?
Since we anticipated there will be a difference between the public and private test data, we also decided to not optimize the QWK coefficients to avoid the risk of overfitting to the public data.
(we used coef = [0.5, 1.5, 2.5, 3.5])
We relied on local validation QWK combined with validation loss and early stopping. We tried to select models that got low validation loss combined with a relatively low difference between the train and validation loss. This proved to be a good tactic as we jumped almost 100 places between the public and private LB.
What Didn't Work
We have tried using Green CLHE preprocessing but got no apparent improvement. We ended up not using it for any of our ensemble models.
Did anyone have any luck with it?
This was our first Computer Vision contest so we are happy with the results. Thanks for everyone involved with the discussions and awesome kernels! we learned a lot!